Oncoradiology, Volume. 34, Issue 3, 266(2025)
Preliminary study on the identification of benign and malignant lung nodules and prediction of pathological types using artificial intelligence software based on CT target scan
[1] [1] BRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263.
[2] [2] MAZZONE P J, SILVESTRI G A, SOUTER L H, et al. Screening for lung cancer: CHEST guideline and expert panel report[J]. Chest, 2021, 160(5): e427-e494.
[4] [4] MOON Y, LEE K Y, PARK J K. The prognosis of invasive adenocarcinoma presenting as ground-glass opacity on chest computed tomography after sublobar resection[J]. J Thorac Dis, 2017, 9(10): 3782-3792.
[5] [5] CHEN P H, CHANG K M, TSENG W C, et al. Invasiveness and surgical timing evaluation by clinical features of ground-glass opacity nodules in lung cancers[J]. Thorac Cancer, 2019, 10(11): 2133-2141.
[6] [6] HUALONG Y, SHIHE L, CHUANYU Z, et al. Computed tomography and pathology evaluation of lung ground-glass opacity[J]. Exp Ther Med, 2018, 16(6): 5305-5309.
[7] [7] KIM Y W, KWON B S, LIM S Y, et al. Lung cancer probability and clinical outcomes of baseline and new subsolid nodules detected on low-dose CT screening[J]. Thorax, 2021, 76(10): 980-988.
[8] [8] WANG B, ZHANG H, LI W, et al. Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study[J]. Front Oncol, 2023, 13: 1037052.
[9] [9] SNOECKX A, REYNTIENS P, DESBUQUOIT D, et al. Evaluation of the solitary pulmonary nodule: size matters, but do not ignore the power of morphology[J]. Insights Imaging, 2018, 9(1): 73-86.
[10] [10] SOHN J H, FIELDS B K K. Radiomics and deep learning to predict pulmonary nodule metastasis at CT[J]. Radiology, 2024, 311(1): e233356.
[11] [11] VAN DER VELDEN B H M, KUIJF H J, GILHUIJS K G A, et al. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis[J]. Med Image Anal, 2022, 79: 102470.
[12] [12] VARGHESE C, RAJAGOPALAN S, KARWOSKI R A, et al. Computed tomography–based score indicative of lung cancer aggression (SILA) predicts the degree of histologic tissue invasion and patient survival in lung adenocarcinoma spectrum[J]. J Thorac Oncol, 2019, 14(8): 1419-1429.
[14] [14] HUANG L Y, LIN W H, XIE D P, et al. Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study[J]. Eur Radiol, 2022, 32(3): 1983-1996.
Get Citation
Copy Citation Text
CHEN Lei, ZHANG Zehua, LUO Rong, XIANG Huijing, LI Ruimin, ZHOU Zhengrong. Preliminary study on the identification of benign and malignant lung nodules and prediction of pathological types using artificial intelligence software based on CT target scan[J]. Oncoradiology, 2025, 34(3): 266
Category:
Received: Jan. 15, 2025
Accepted: Aug. 22, 2025
Published Online: Aug. 22, 2025
The Author Email: ZHOU Zhengrong (zhouzr_16@163.com)